Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies. (1st May 2022)

• Record Type: Journal Article
• Authors: Neshat, Mehdi; Majidi Nezhad, Meysam; Mirjalili, Seyedali; Piras, Giuseppe; Garcia, Davide Astiaso
• Is Part Of: Energy conversion and management. Volume 259(2022)
• Publication Date: 2022-05-01
• Subjects: Wind speed -- Short-term forecasting -- Optimisation algorithms -- Quaternion Convolutional neural network -- Deep learning models -- Hyper-parameters tuning; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2022.115590
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A novel Quaternion Convolutional model is proposed for wind speed forecasting. An adaptive variational mode decomposition method is introduced. New hyper-parameter tuner is proposed: Differential Arithmetic Optimisation Algorithm. The performance of the proposed model is validated on two case studies. Abstract: An accurate prediction of short-term and long-term wind speed is necessary in order to integrate wind energy into large-scale grid power. However, wind speed presents diverse and complex seasonal and stochastic characteristics that impose challenges on wind speed forecasting models. This study proposes a Quaternion Convolutional Neural Network combined with a Bi-directional Long Short-Term Memory recurrent network to forecast wind speed. Quaternion Convolutional Neural Network is used to elicit more effective features from the stochastic sub-signals of wind speed. A new decomposition method is also proposed, comprising variational mode decomposition to decompose the wind speed data into optimal signal components, and an improved arithmetic optimisation algorithm to optimise the parameters of the variational mode decomposition. Furthermore, a fast and effective hyper-parameters tuner is introduced in order to adjust the hyper-parameters and architecture of the proposed hybrid forecasting model. The proposed forecasting model is developed based on data collected from Lesvos and Samothraki Greek islands located in the North Aegean Sea with the forecasting range in one-day ahead (long-term) and achieved considerable accuracy improvements in these case studies compared with the bi-directional long short-term memory model at 13 % and 20 %, respectively. The experimental outcomes confirm that, first, the proposed hybrid forecasting model considerably outperforms the five existing machine learning and two hybrid models in terms of precision and stability.